Back

Astroglial Dysfunction, Demyelination and Nodular inflammation in Necrotizing Meningoencephalitis

Hernandez, S. I.; Assenmacher, C.-A.; Church, M. E.; Alvarez, J. I.

2024-11-14 neuroscience
10.1101/2024.11.11.623107 bioRxiv
Show abstract

Necrotizing Meningoencephalitis (NME), a form of Meningoencephalitis of Unknown Origin (MUO), is a progressive neuroinflammatory disease that primarily affects young, small-breed dogs. Due to limited understanding of its pathophysiology, early detection and the development of targeted therapies remain challenging. Definitive ante-mortem diagnosis is often unfeasible, and dogs with NME are frequently grouped under the broader MUO category. Our long-term objective is to identify distinct disease mechanisms within each MUO subtype to improve diagnostic accuracy, therapeutic approaches, and prognostic outcomes. To establish unique inflammatory patterns as they relate to neuropathologic changes in NME, we studied we studied the degree of immune cell infiltration, astrogliosis, demyelination, and microglial activation, comparing these factors with granulomatous meningoencephalomyelitis (GME), a closely related MUO subtype. We found that in the leptomeninges, NME is characterized by mild immune cell infiltration, in contrast to the prominent, B cell-rich aggregates seen in GME. In the neuroparenchyma, both diseases exhibit a comparable degree of lymphocyte infiltration; however, demyelination is more pronounced in NME, particularly within the subcortical white matter. Notably, areas of the brain affected by NME display a reduction in astrogliosis, which is associated with a marked decrease in the expression of the water channel protein aquaporin-4 (AQP4), a reduction not observed in GME. Additionally, we found that AQP4 expression levels correlate with the extent of microglial and macrophage activation. These findings suggest that astrocyte dysfunction in regions of microglial inflammation is a driver of NME and with adaptive immune responses likely playing a supportive role.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.